Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
Video Behavior Profiling for Anomaly Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene Segmentation for Behaviour Correlation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning semantic scene models by trajectory analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Learning semantic scene models from observing activity in visual surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
International Journal of Computer Vision
Exploiting sparse representations for robust analysis of noisy complex video scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
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Topic models such as Latent Dirichlet Allocation (LDA) are used extensively for modelling multi-object behaviour and anomaly detection in busy scenes. However, existing topic models suffer from the sensitivity problem, where they are unable to detect anomalies that are mixed in with large numbers of co-occurring normal behaviours. Also at issue is the localisation problem, where anomalies are detected but not localised within a given video clip. To address these two problems this paper proposes a novel region LDA model, which encodes the spatial awareness that is ignored by conventional topic models. Both scene decomposition and behavioural modelling are simultaneously performed. Consequentially, abnormality is detected per-region rather than for the entire scene, resolving both the sensitivity and localisation issues. Experiments conducted on busy real world scenes demonstrate the superiority of the proposed model.